AI Assisted matching in Mergers And Acquisitions

Abstract

Traditional buyer identification in M&A relies on manual screening and professional networks, making it resource-intensive and naturally limiting the buyer pool. This thesis investigates whether textual embedding models can support the identification of relevant potential buyers in mergers and acquisitions. The study examines how different representation methods, including TF-IDF, Doc2Vec with smooth inverse frequency weighting, and Transformer based models, capture similarity between companies when applied to standardized summaries of portfolio company descriptions. The summaries are created using a large language model with information provided on the portfolio companies websites. The performance of the embedding models is evaluated through visualization of the embedding spaces, cosine similarity search experiments, and an expert review of buyer recommendations. The results indicate that TF-IDF and the Transformer model produced relevant recommendations, with the Transformer model demonstrating the best performance in embedding space separation and alignment with expert judgment, while Doc2Vec models showed weaker differentiation between company types. Overall, the study shows that embedding based similarity search can serve as a useful first step in buyer discovery by expanding the range of potential buyers considered and improving efficiency. The work also highlights that further validation across a larger set of targets and with a more complete dataset would strengthen confidence in these results.